论文标题
授权积极学习以共同优化系统和用户需求
Empowering Active Learning to Jointly Optimize System and User Demands
论文作者
论文摘要
现有的积极学习方法通过对未标记的注释进行抽样实例来最大程度地提高系统性能，从而产生最有效的培训。但是，当主动学习与最终用户应用程序集成在一起时，这可能会导致参与用户的挫败感，因为他们花了时间标记他们对阅读不感兴趣的实例。在本文中，我们提出了一种新的主动学习方法，该方法共同优化了活跃学习系统（有效培训）和用户（接收有用实例）的看似抵消目标。我们在教育应用中研究我们的方法，该方法尤其受益于该技术，因为该系统需要快速学会预测对特定用户的锻炼的适当性，而用户应仅收到与其技能相匹配的练习。我们使用来自真实用户的数据评估多种学习策略和用户类型，并发现当其他方法为最终用户提供许多不合适的练习时，我们的联合方法可以更好地满足这两个目标。
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
